Trust In Governments And Health Workers Low Globally, Influencing Attitudes Toward Health Information, Vaccines
Bibliographic record
Abstract
Trust, particularly during emergencies, is essential for effective health care delivery and health policy implementation. We used data from the 2018 Wellcome Global Monitor survey (comprising nationally representative samples from 144 countries) to examine levels and correlates of trust in governments and health workers and attitudes toward vaccines. Only one-quarter of respondents globally expressed a lot of trust in their government (trust was more common among people with less schooling, those living in rural areas, those who were financially comfortable, and those who were older), and fewer than half of respondents globally said that they trust doctors and nurses a lot. People's trust in these institutions was correlated with trust in health or medical advice from them, and with more positive attitudes toward vaccines. Vaccine enthusiasm varied substantially across regions, with safety being the most common concern. Policy makers should understand that the public may have varying levels of trust in different institutions and actors. Although much attention is paid to crafting public health messages, it may be equally important, especially during a pandemic, to identify appropriate, trusted messengers to deliver those messages more effectively to different target populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".